A Framework for Hardware Trojan Vulnerability Estimation and Localization in RTL Designs

Islam, Sheikh Ariful; Sah, Love Kumar; Katkoori, Srinivas

doi:10.1007/s41635-020-00100-2

Cited by 6 publications

(11 citation statements)

References 36 publications

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“…Firstly, the dataset and experiment conditions are very varied among different works. For example, [31], [7], [32], [2] papers propose various ideas to locate the HT nodes in the circuit, and they demonstrate promising [34] Pre-S Yes No High Low HT localization with 97.3% accuracy and less than 2% false positive Code analysis [5] Pre-S Yes No Low Low Activity estimation with less than 2% error to flag low-activity as HT VeriTrust [32] Pre-S Yes No Low Low HT localization with 100% recall and 11.5% precision FANCI [7] Pre-S Yes No Low Low HT localization with 100% recall and less than 8% false positive UCI [31] Pre-S Yes No Low Low HT localization with 100% recall and 7.5% precision Symbolic algebra [2] Pre-S No No High High HT localization with 100% recall and 74% precision Thermal map [3] Post-S results on their limited sets of benchmarks. However, each one reveals the shortcomings of the former method against distinct Trojans.…”

Section: B Comparing Ht Localization Methodsmentioning

confidence: 99%

“…Due to exclusive definition of HT, later [6] defeats [31] and [32] and [7] get bypassed by the new HT attack [33]. [5] proposes a framework to analyze RTL codes using word-level statistics of the inputs. It locates the arithmetic blocks with rare nets to be reviewed as candidates vulnerable to HT and can only identify HTs that are always on or triggered by current inputs.…”

Section: B Hardware Trojan Localizationmentioning

confidence: 99%

“…HT defense methods based on formal verification and code analysis define some properties for HT, analyze the hardware design, and mark the areas satisfying the predefined properties. For example, [5] examines RTL codes using word-level statistics of the inputs and tags the arithmetic blocks with rare nets as vulnerable to HT. [6] flags the unused portion of the circuit as malicious.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Golden Reference-Free Hardware Trojan Localization Using Graph Convolutional Network

Yasaei¹,

Faezi²,

Faruque³

2022

IEEE Trans. VLSI Syst.

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The globalization of the Integrated Circuit (IC) supply chain has moved most of the design, fabrication, and testing process from a single trusted entity to various untrusted thirdparty entities worldwide. The risk of using untrusted third-Party Intellectual Property (3PIP) is the possibility for adversaries to insert malicious modifications known as Hardware Trojans (HTs). These HTs can compromise the integrity, deteriorate the performance, deny the service, and alter the functionality of the design. While numerous HT detection methods have been proposed in the literature, the crucial task of HT localization is overlooked. Moreover, a few existing HT localization methods have several weaknesses: reliance on a golden reference, inability to generalize for all types of HT, lack of scalability, low localization resolution, and manual feature engineering/property definition. To overcome their shortcomings, we propose a novel, golden reference-free HT localization method at the pre-silicon stage by leveraging Graph Convolutional Network (GCN). In this work, we convert the circuit design to its intrinsic data structure, graph and extract the node attributes. Afterward, the graph convolution performs automatic feature extraction for nodes to classify the nodes as Trojan or benign. Our approach is automated and does not burden the designer with manual code review. It locates the Trojan signals with 99.6% accuracy, 93.1% F1-score, and a false-positive rate below 0.009%.

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Section: B Comparing Ht Localization Methodsmentioning

confidence: 99%

Section: B Hardware Trojan Localizationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Golden Reference-Free Hardware Trojan Localization Using Graph Convolutional Network

Yasaei¹,

Faezi²,

Faruque³

2022

IEEE Trans. VLSI Syst.

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“…S.A. Islam et al. [14, 15] presented an HT vulnerability analysis for the macro block in the RTL design based on rare triggering nets’ estimation. Zhengfei Jin et al.…”

Section: Introductionmentioning

confidence: 99%

“…Choo et al [13] proposed the Minimum Redundancy Maximum Relevance feature selection to improve the effectiveness of RTL Trojan features. S.A. Islam et al [14,15] presented an HT vulnerability analysis for the macro block in the RTL design based on rare triggering nets' estimation. Zhengfei Jin et al [16] proposed the RTL nodes' partition method and novel features, applying the random forest algorithm to detect RTL Trojans effectively.…”

Section: Introductionmentioning

confidence: 99%

Blinding HT: Hiding Hardware Trojan signals traced across multiple sequential levels

Zhang

Chen

et al. 2021

IET Circuits, Devices & Syst

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Modern electronic systems usually use third-party IP cores to build basic blocks. However, there may be Hardware Trojans (HTs) in IP cores, which will cause critical security problem. There are already many HT detection methods which claim to detect all publicly available HT benchmarks. But these methods can still be defeated by designing novel HTs. In this article, a method called Blinding HT is proposed, which camouflages itself as a normal circuit and is difficult to be triggered. The Blinding HT hides input signals of HT modules by tracing across multiple sequential levels. This method increases the influence of HT trigger inputs on output signals, so that trigger inputs are not be identified as redundant inputs. In this way, this approach can defeat the detection methods which identify weakly affecting trigger inputs and redundant trigger inputs across multiple sequential levels. As shown in the experimental results, the proposed HTs are hardly detected even by the novel HT detection approach based on machine learning algorithm. These HTs have small footprints on the design in terms of area and power to resist the side-channel effect analysis. The proposed HT has stealthiness, general applicability and imperceptibility.This is an open access article under the terms of the Creative Commons Attribution-NonCommercial-NoDerivs License, which permits use and distribution in any medium, provided the original work is properly cited, the use is non-commercial and no modifications or adaptations are made.

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Hardware Trojan Localization: Modeling and Empirical Approach

Islam

Katkoori

2021

Behavioral Synthesis for Hardware Security

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A Framework for Hardware Trojan Vulnerability Estimation and Localization in RTL Designs

Cited by 6 publications

References 36 publications

Golden Reference-Free Hardware Trojan Localization Using Graph Convolutional Network

Golden Reference-Free Hardware Trojan Localization Using Graph Convolutional Network

Blinding HT: Hiding Hardware Trojan signals traced across multiple sequential levels

Hardware Trojan Localization: Modeling and Empirical Approach

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